Abstract
Clustering is often classified as an unsupervised machine learning approach. It has been extensively used for data analysis. It permits us to extract some structure from a data set. Clustering can be applied to all types of data.
A larger number of clustering algorithms have been proposed differing on the type of data we have and the type of structure we extract from the data. The type of structure in use has naturally implications on how we represent membership of elements to the structure, and also on how we assign (classify) new elements to the structure. Fuzzy sets (including its variants) have been used for building data models. In particular, fuzzy partitions (fuzzy c-means like, possibilistic), I-fuzzy, and H-fuzzy partitions are examples of structures used to represent the output of fuzzy clustering.
In this paper we review some types of fuzzy partitions that we have considered in previous works.
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Torra, V., Jurío, A., Bustince, H., Aliahmadipour, L. (2020). Fuzzy Sets in Clustering: On Fuzzy Partitions. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_3
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DOI: https://doi.org/10.1007/978-3-030-23756-1_3
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